"Finding tasks for large language model agents" matching MCP servers:

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

Enables an AI assistant to open 3D model files (STL, 3MF, Bambu Studio and OrcaSlicer projects) in a local browser viewer where the user clicks exact spots to leave feedback. Each note comes back with the part name, millimetre coordinates, surface direction and a numbered screenshot, so the assistant can revise the design and show the new version.

Gives LLM agents spatial awareness inside Autodesk Maya, enabling scene inspection and measurement, aesthetic and engineering audits, camera planning, checkpoint/rollback safety, asset import, and viewport or render previews. Agents can also execute arbitrary Python in Maya to build and verify 3D scenes against engineering rules instead of writing blind.

Enables AI-driven 3D modeling and scene creation in Blender, allowing LLMs to create, modify, and manipulate objects, materials, and assets through natural language.

Ask, check and change live Revit models from Claude, Cursor or any MCP client. One undo per change, read-only switch. Revit 2022-2027.

Connects MCP-compatible AI assistants to Azgaar's Fantasy Map Generator so they can view, inspect, edit, and export fantasy maps in a live local browser, including borders, states, terrain, layers, and files. It runs locally and offline after installation, supports collaborative manual editing, undo, autosave, and text-only map output for models that cannot see images.

A local, offline MCP server that enables AI agents to collaborate with SketchUp for controlled modeling, editing, and review tasks via 41 MCP tools.

Open, secure runtime that puts Revit, Rhino, Navisworks, and Power BI behind one MCP call center, enabling an AI agent to orchestrate them directly instead of manually dumping data between disconnected apps.

Enables users to generate rigged, game-ready gun models in Blender from parameterized presets or platforms, including attachments, animation clips, fact sheets, and reference-photo validation.

Enables AI assistants to inspect and organize Adobe Premiere Pro projects, edit timelines with frame-accurate cuts and trims, animate motion and opacity keyframes, and trigger exports through natural language.

Enables controlling CLO3D from any MCP client to inspect projects, create and edit patterns, manage fabrics and colorways, run simulations, and export models through chat.

Enables AI assistants to programmatically inspect projects, create compositions and layers, apply effects, animate properties, and manage render jobs in Adobe After Effects through natural language.

Local MCP bridge that exposes the ChatGPT Figma connector to OpenCode without launching Codex, using the existing authenticated session.

Translates natural language commands into structured geometric operations for a simulated CAD engine using LLMs. It enables users to create shapes, modify dimensions, and perform 3D operations like extrusion through the Model Context Protocol.

Enables AI assistants to directly control and interact with the Unreal Editor through natural language, allowing tasks such as spawning actors, building levels, creating materials, and running Python scripts.

Enables AI coding agents to read and write a user's Figma file through the Figma Plugin API, offline and privately, without API tokens or rate limits.

Node.js MCP server enabling AI assistants to interact with Autodesk Revit for model operations, data queries, and sketch-to-building generation via LLM.

Connects MCP-aware AI clients to a live SketchUp session so users can create and modify 3D models, apply materials, perform booleans and joinery, and export scenes through natural-language prompts.

MCP server that exposes the @bunge/ds-components design system catalog, allowing AI assistants to list, search, and retrieve component details including inputs, outputs, usage examples, and import instructions.

Enables Claude Desktop to control FreeCAD for 3D CAD modeling, including creating, editing, and deleting objects, executing Python code, and running FEM analyses.